{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hand-crafted features for GTZAN\n",
    "\n",
    "> The goal of this notebook is to create several audio features descriptors for the GTZAN dataset, as proposed for many year as input for machine learning algorithms. We are going to use timbral texture based features and tempo based features for this. The main goal is to produce this features, classify and then compare with our proposed deep learning approach, using CNNs on the raw audio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import librosa\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import kurtosis\n",
    "from scipy.stats import skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "import lightgbm as lgbm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the seed\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gtzan_dir = '../data/genres/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "song_samples = 22050*30\n",
    "genres = {'metal': 0, 'disco': 1, 'classical': 2, 'hiphop': 3, 'jazz': 4, \n",
    "          'country': 5, 'pop': 6, 'blues': 7, 'reggae': 8, 'rock': 9}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_features(y, sr, n_fft = 1024, hop_length = 512):\n",
    "    # Features to concatenate in the final dictionary\n",
    "    features = {'centroid': None, 'roloff': None, 'flux': None, 'rmse': None,\n",
    "                'zcr': None, 'contrast': None, 'bandwidth': None, 'flatness': None}\n",
    "    \n",
    "    # Count silence\n",
    "    if 0 < len(y):\n",
    "        y_sound, _ = librosa.effects.trim(y, frame_length=n_fft, hop_length=hop_length)\n",
    "    features['sample_silence'] = len(y) - len(y_sound)\n",
    "\n",
    "    # Using librosa to calculate the features\n",
    "    features['centroid'] = librosa.feature.spectral_centroid(y, sr=sr, n_fft=n_fft, hop_length=hop_length).ravel()\n",
    "    features['roloff'] = librosa.feature.spectral_rolloff(y, sr=sr, n_fft=n_fft, hop_length=hop_length).ravel()\n",
    "    features['zcr'] = librosa.feature.zero_crossing_rate(y, frame_length=n_fft, hop_length=hop_length).ravel()\n",
    "    features['rmse'] = librosa.feature.rms(y, frame_length=n_fft, hop_length=hop_length).ravel()\n",
    "    features['flux'] = librosa.onset.onset_strength(y=y, sr=sr).ravel()\n",
    "    features['contrast'] = librosa.feature.spectral_contrast(y, sr=sr).ravel()\n",
    "    features['bandwidth'] = librosa.feature.spectral_bandwidth(y, sr=sr, n_fft=n_fft, hop_length=hop_length).ravel()\n",
    "    features['flatness'] = librosa.feature.spectral_flatness(y, n_fft=n_fft, hop_length=hop_length).ravel()\n",
    "    \n",
    "    # MFCC treatment\n",
    "    mfcc = librosa.feature.mfcc(y, n_fft = n_fft, hop_length = hop_length, n_mfcc=13)\n",
    "    for idx, v_mfcc in enumerate(mfcc):\n",
    "        features['mfcc_{}'.format(idx)] = v_mfcc.ravel()\n",
    "        \n",
    "    # Get statistics from the vectors\n",
    "    def get_moments(descriptors):\n",
    "        result = {}\n",
    "        for k, v in descriptors.items():\n",
    "            result['{}_max'.format(k)] = np.max(v)\n",
    "            result['{}_min'.format(k)] = np.min(v)\n",
    "            result['{}_mean'.format(k)] = np.mean(v)\n",
    "            result['{}_std'.format(k)] = np.std(v)\n",
    "            result['{}_kurtosis'.format(k)] = kurtosis(v)\n",
    "            result['{}_skew'.format(k)] = skew(v)\n",
    "        return result\n",
    "    \n",
    "    dict_agg_features = get_moments(features)\n",
    "    dict_agg_features['tempo'] = librosa.beat.tempo(y, sr=sr)[0]\n",
    "    \n",
    "    return dict_agg_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_process_songs(src_dir, debug = True):    \n",
    "    # Empty array of dicts with the processed features from all files\n",
    "    arr_features = []\n",
    "\n",
    "    # Read files from the folders\n",
    "    for x,_ in genres.items():\n",
    "        folder = src_dir + x\n",
    "        \n",
    "        for root, subdirs, files in os.walk(folder):\n",
    "            for file in files:\n",
    "                # Read the audio file\n",
    "                file_name = folder + \"/\" + file\n",
    "                signal, sr = librosa.load(file_name)\n",
    "                \n",
    "                # Debug process\n",
    "                if debug:\n",
    "                    print(\"Reading file: {}\".format(file_name))\n",
    "                \n",
    "                # Append the result to the data structure\n",
    "                features = get_features(signal, sr)\n",
    "                features['genre'] = genres[x]\n",
    "                arr_features.append(features)\n",
    "    return arr_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 5min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Get list of dicts with features and convert to dataframe\n",
    "features = read_process_songs(gtzan_dir, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = pd.DataFrame(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 134)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centroid_max</th>\n",
       "      <th>centroid_min</th>\n",
       "      <th>centroid_mean</th>\n",
       "      <th>centroid_std</th>\n",
       "      <th>centroid_kurtosis</th>\n",
       "      <th>centroid_skew</th>\n",
       "      <th>roloff_max</th>\n",
       "      <th>roloff_min</th>\n",
       "      <th>roloff_mean</th>\n",
       "      <th>roloff_std</th>\n",
       "      <th>...</th>\n",
       "      <th>mfcc_11_kurtosis</th>\n",
       "      <th>mfcc_11_skew</th>\n",
       "      <th>mfcc_12_max</th>\n",
       "      <th>mfcc_12_min</th>\n",
       "      <th>mfcc_12_mean</th>\n",
       "      <th>mfcc_12_std</th>\n",
       "      <th>mfcc_12_kurtosis</th>\n",
       "      <th>mfcc_12_skew</th>\n",
       "      <th>tempo</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5514.393296</td>\n",
       "      <td>1789.366583</td>\n",
       "      <td>3176.995746</td>\n",
       "      <td>550.485825</td>\n",
       "      <td>1.277930</td>\n",
       "      <td>0.851571</td>\n",
       "      <td>9905.273438</td>\n",
       "      <td>4005.175781</td>\n",
       "      <td>6235.702536</td>\n",
       "      <td>1316.782580</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.381803</td>\n",
       "      <td>-0.087361</td>\n",
       "      <td>20.820358</td>\n",
       "      <td>-32.647717</td>\n",
       "      <td>-4.103275</td>\n",
       "      <td>8.186303</td>\n",
       "      <td>0.171622</td>\n",
       "      <td>-0.170111</td>\n",
       "      <td>95.703125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5626.545444</td>\n",
       "      <td>1763.465470</td>\n",
       "      <td>2850.163030</td>\n",
       "      <td>450.508356</td>\n",
       "      <td>7.136744</td>\n",
       "      <td>1.872261</td>\n",
       "      <td>10314.404297</td>\n",
       "      <td>4155.908203</td>\n",
       "      <td>5926.993343</td>\n",
       "      <td>852.511470</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.122149</td>\n",
       "      <td>0.049450</td>\n",
       "      <td>19.133057</td>\n",
       "      <td>-32.525146</td>\n",
       "      <td>-10.894270</td>\n",
       "      <td>7.584343</td>\n",
       "      <td>0.193536</td>\n",
       "      <td>0.236081</td>\n",
       "      <td>151.999081</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4137.382271</td>\n",
       "      <td>1196.751465</td>\n",
       "      <td>2147.049597</td>\n",
       "      <td>394.428946</td>\n",
       "      <td>3.603367</td>\n",
       "      <td>1.476032</td>\n",
       "      <td>7751.953125</td>\n",
       "      <td>2842.382812</td>\n",
       "      <td>4376.752601</td>\n",
       "      <td>842.133273</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.228953</td>\n",
       "      <td>-0.074895</td>\n",
       "      <td>11.926430</td>\n",
       "      <td>-40.928551</td>\n",
       "      <td>-14.248710</td>\n",
       "      <td>7.786307</td>\n",
       "      <td>0.161245</td>\n",
       "      <td>-0.130394</td>\n",
       "      <td>112.347147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4141.859853</td>\n",
       "      <td>1554.571724</td>\n",
       "      <td>2700.584556</td>\n",
       "      <td>411.301524</td>\n",
       "      <td>-0.256259</td>\n",
       "      <td>0.124371</td>\n",
       "      <td>7708.886719</td>\n",
       "      <td>3531.445312</td>\n",
       "      <td>5178.110839</td>\n",
       "      <td>679.032203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157389</td>\n",
       "      <td>-0.127252</td>\n",
       "      <td>16.922089</td>\n",
       "      <td>-34.641380</td>\n",
       "      <td>-13.005329</td>\n",
       "      <td>6.983782</td>\n",
       "      <td>0.370824</td>\n",
       "      <td>0.127291</td>\n",
       "      <td>99.384014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6472.152579</td>\n",
       "      <td>2069.514147</td>\n",
       "      <td>3357.385889</td>\n",
       "      <td>548.509155</td>\n",
       "      <td>1.316912</td>\n",
       "      <td>0.691435</td>\n",
       "      <td>10056.005859</td>\n",
       "      <td>4198.974609</td>\n",
       "      <td>6810.304320</td>\n",
       "      <td>964.532861</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.134480</td>\n",
       "      <td>0.061888</td>\n",
       "      <td>23.652538</td>\n",
       "      <td>-27.224190</td>\n",
       "      <td>-3.429460</td>\n",
       "      <td>7.443413</td>\n",
       "      <td>0.033728</td>\n",
       "      <td>0.119981</td>\n",
       "      <td>135.999178</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   centroid_max  centroid_min  centroid_mean  centroid_std  centroid_kurtosis  \\\n",
       "0   5514.393296   1789.366583    3176.995746    550.485825           1.277930   \n",
       "1   5626.545444   1763.465470    2850.163030    450.508356           7.136744   \n",
       "2   4137.382271   1196.751465    2147.049597    394.428946           3.603367   \n",
       "3   4141.859853   1554.571724    2700.584556    411.301524          -0.256259   \n",
       "4   6472.152579   2069.514147    3357.385889    548.509155           1.316912   \n",
       "\n",
       "   centroid_skew    roloff_max   roloff_min  roloff_mean   roloff_std  ...  \\\n",
       "0       0.851571   9905.273438  4005.175781  6235.702536  1316.782580  ...   \n",
       "1       1.872261  10314.404297  4155.908203  5926.993343   852.511470  ...   \n",
       "2       1.476032   7751.953125  2842.382812  4376.752601   842.133273  ...   \n",
       "3       0.124371   7708.886719  3531.445312  5178.110839   679.032203  ...   \n",
       "4       0.691435  10056.005859  4198.974609  6810.304320   964.532861  ...   \n",
       "\n",
       "   mfcc_11_kurtosis  mfcc_11_skew  mfcc_12_max  mfcc_12_min  mfcc_12_mean  \\\n",
       "0         -0.381803     -0.087361    20.820358   -32.647717     -4.103275   \n",
       "1         -0.122149      0.049450    19.133057   -32.525146    -10.894270   \n",
       "2         -0.228953     -0.074895    11.926430   -40.928551    -14.248710   \n",
       "3          0.157389     -0.127252    16.922089   -34.641380    -13.005329   \n",
       "4         -0.134480      0.061888    23.652538   -27.224190     -3.429460   \n",
       "\n",
       "   mfcc_12_std  mfcc_12_kurtosis  mfcc_12_skew       tempo  genre  \n",
       "0     8.186303          0.171622     -0.170111   95.703125      0  \n",
       "1     7.584343          0.193536      0.236081  151.999081      0  \n",
       "2     7.786307          0.161245     -0.130394  112.347147      0  \n",
       "3     6.983782          0.370824      0.127291   99.384014      0  \n",
       "4     7.443413          0.033728      0.119981  135.999178      0  \n",
       "\n",
       "[5 rows x 134 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features.to_csv('../data/gtzan_features.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_features.drop(['genre'], axis=1).values\n",
    "y = df_features['genre'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "\n",
    "> Linear (and nonlinear) dimensionality reduction of the GTZAN features for visualization purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standartize the dataset\n",
    "scale = StandardScaler()\n",
    "x_scaled = scale.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cumulative explained variance ratio = 0.8737\n"
     ]
    }
   ],
   "source": [
    "# Use PCA only for visualization\n",
    "pca = PCA(n_components=35, whiten=True)\n",
    "x_pca = pca.fit_transform(x_scaled)\n",
    "print(\"cumulative explained variance ratio = {:.4f}\".format(np.sum(pca.explained_variance_ratio_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use LDA only for visualization\n",
    "lda = LDA()\n",
    "x_lda = lda.fit_transform(x_scaled, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 1000 samples in 0.018s...\n",
      "[t-SNE] Computed neighbors for 1000 samples in 0.230s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 1000\n",
      "[t-SNE] Mean sigma: 3.258477\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 75.083298\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.028246\n"
     ]
    }
   ],
   "source": [
    "# Using tsne\n",
    "tsne = TSNE(n_components=2, verbose=1, learning_rate=250)\n",
    "x_tsne = tsne.fit_transform(x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 4))\n",
    "plt.subplot(131)\n",
    "plt.scatter(x_pca[:,0], x_pca[:,1], c=y)\n",
    "plt.colorbar()\n",
    "plt.title(\"Embedded space with PCA\")\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.scatter(x_lda[:,0], x_lda[:,1], c=y)\n",
    "plt.colorbar()\n",
    "plt.title(\"Embedded space with LDA\")\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.scatter(x_tsne[:,0], x_tsne[:,1], c=y)\n",
    "plt.colorbar()\n",
    "plt.title(\"Embedded space with TSNE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classical Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper to plot confusion matrix -- from Scikit-learn website\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('scale',\n",
       "                                        StandardScaler(copy=True,\n",
       "                                                       with_mean=True,\n",
       "                                                       with_std=True)),\n",
       "                                       ('var_tresh',\n",
       "                                        VarianceThreshold(threshold=0.15999999999999998)),\n",
       "                                       ('feature_selection',\n",
       "                                        SelectFromModel(estimator=LGBMClassifier(boosting_type='gbdt',\n",
       "                                                                                 class_weight=None,\n",
       "                                                                                 colsample_bytree=1.0,\n",
       "                                                                                 importance_type='split',\n",
       "                                                                                 lear...\n",
       "                                                           l1_ratio=None,\n",
       "                                                           max_iter=100,\n",
       "                                                           multi_class='auto',\n",
       "                                                           n_jobs=None,\n",
       "                                                           penalty='l2',\n",
       "                                                           random_state=None,\n",
       "                                                           solver='lbfgs',\n",
       "                                                           tol=0.0001,\n",
       "                                                           verbose=0,\n",
       "                                                           warm_start=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=6,\n",
       "             param_grid={'cls__C': [0.5, 1, 2, 5], 'cls__max_iter': [500],\n",
       "                         'cls__penalty': ['l1', 'l2']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    \"cls__penalty\": [\"l1\", \"l2\"],\n",
    "    \"cls__C\": [0.5, 1, 2, 5],\n",
    "    \"cls__max_iter\": [500]\n",
    "}\n",
    "\n",
    "pipe_lr = Pipeline([\n",
    "    ('scale', StandardScaler()),\n",
    "    ('var_tresh', VarianceThreshold(threshold=(.8 * (1 - .8)))),\n",
    "    ('feature_selection', SelectFromModel(lgbm.LGBMClassifier())),\n",
    "    ('cls', LogisticRegression())\n",
    "])\n",
    "\n",
    "grid_lr = GridSearchCV(pipe_lr, params, scoring='accuracy', n_jobs=6, cv=5)\n",
    "grid_lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score on validation set (accuracy) = 0.7187\n",
      "best score on test set (accuracy) = 0.7640\n"
     ]
    }
   ],
   "source": [
    "preds = grid_lr.predict(X_test)\n",
    "print(\"best score on validation set (accuracy) = {:.4f}\".format(grid_lr.best_score_))\n",
    "print(\"best score on test set (accuracy) = {:.4f}\".format(accuracy_score(y_test, preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('scale',\n",
       "                                        StandardScaler(copy=True,\n",
       "                                                       with_mean=True,\n",
       "                                                       with_std=True)),\n",
       "                                       ('var_tresh',\n",
       "                                        VarianceThreshold(threshold=0.15999999999999998)),\n",
       "                                       ('feature_selection',\n",
       "                                        SelectFromModel(estimator=LGBMClassifier(boosting_type='gbdt',\n",
       "                                                                                 class_weight=None,\n",
       "                                                                                 colsample_bytree=1.0,\n",
       "                                                                                 importance_type='split',\n",
       "                                                                                 lear...\n",
       "                                                      n_jobs=None, penalty='l2',\n",
       "                                                      power_t=0.5,\n",
       "                                                      random_state=None,\n",
       "                                                      shuffle=True, tol=0.001,\n",
       "                                                      validation_fraction=0.1,\n",
       "                                                      verbose=0,\n",
       "                                                      warm_start=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=6,\n",
       "             param_grid={'cls__l1_ratio': [0.15, 0.25, 0.5, 0.75],\n",
       "                         'cls__loss': ['log'], 'cls__penalty': ['elasticnet']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    \"cls__loss\": ['log'],\n",
    "    \"cls__penalty\": [\"elasticnet\"],\n",
    "    \"cls__l1_ratio\": [0.15, 0.25, 0.5, 0.75],\n",
    "}\n",
    "\n",
    "pipe_en = Pipeline([\n",
    "    ('scale', StandardScaler()),\n",
    "    ('var_tresh', VarianceThreshold(threshold=(.8 * (1 - .8)))),\n",
    "    ('feature_selection', SelectFromModel(lgbm.LGBMClassifier())),\n",
    "    ('cls', SGDClassifier())\n",
    "])\n",
    "\n",
    "grid_en = GridSearchCV(pipe_en, params, scoring='accuracy', n_jobs=6, cv=5)\n",
    "grid_en.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score on validation set (accuracy) = 0.6747\n",
      "best score on test set (accuracy) = 0.6880\n"
     ]
    }
   ],
   "source": [
    "preds = grid_en.predict(X_test)\n",
    "print(\"best score on validation set (accuracy) = {:.4f}\".format(grid_en.best_score_))\n",
    "print(\"best score on test set (accuracy) = {:.4f}\".format(accuracy_score(y_test, preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('var_tresh',\n",
       "                                        VarianceThreshold(threshold=0.15999999999999998)),\n",
       "                                       ('feature_selection',\n",
       "                                        SelectFromModel(estimator=LGBMClassifier(boosting_type='gbdt',\n",
       "                                                                                 class_weight=None,\n",
       "                                                                                 colsample_bytree=1.0,\n",
       "                                                                                 importance_type='split',\n",
       "                                                                                 learning_rate=0.1,\n",
       "                                                                                 max_depth=-1,\n",
       "                                                                                 min_child_samples=20,\n",
       "                                                                                 min_child_weight=...\n",
       "                                                               min_impurity_split=None,\n",
       "                                                               min_samples_leaf=1,\n",
       "                                                               min_samples_split=2,\n",
       "                                                               min_weight_fraction_leaf=0.0,\n",
       "                                                               presort='deprecated',\n",
       "                                                               random_state=None,\n",
       "                                                               splitter='best'))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=6,\n",
       "             param_grid={'cls__criterion': ['gini', 'entropy'],\n",
       "                         'cls__splitter': ['best', 'random']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    \"cls__criterion\": [\"gini\", \"entropy\"],\n",
    "    \"cls__splitter\": [\"best\", \"random\"],\n",
    "}\n",
    "\n",
    "pipe_cart = Pipeline([\n",
    "    ('var_tresh', VarianceThreshold(threshold=(.8 * (1 - .8)))),\n",
    "    ('feature_selection', SelectFromModel(lgbm.LGBMClassifier())),\n",
    "    ('cls', DecisionTreeClassifier())\n",
    "])\n",
    "\n",
    "grid_cart = GridSearchCV(pipe_cart, params, scoring='accuracy', n_jobs=6, cv=5)\n",
    "grid_cart.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score on validation set (accuracy) = 0.4947\n",
      "best score on test set (accuracy) = 0.5160\n"
     ]
    }
   ],
   "source": [
    "preds = grid_cart.predict(X_test)\n",
    "print(\"best score on validation set (accuracy) = {:.4f}\".format(grid_cart.best_score_))\n",
    "print(\"best score on test set (accuracy) = {:.4f}\".format(accuracy_score(y_test, preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('var_tresh',\n",
       "                                        VarianceThreshold(threshold=0.15999999999999998)),\n",
       "                                       ('feature_selection',\n",
       "                                        SelectFromModel(estimator=LGBMClassifier(boosting_type='gbdt',\n",
       "                                                                                 class_weight=None,\n",
       "                                                                                 colsample_bytree=1.0,\n",
       "                                                                                 importance_type='split',\n",
       "                                                                                 learning_rate=0.1,\n",
       "                                                                                 max_depth=-1,\n",
       "                                                                                 min_child_samples=20,\n",
       "                                                                                 min_child_weight=...\n",
       "                                                               min_weight_fraction_leaf=0.0,\n",
       "                                                               n_estimators=100,\n",
       "                                                               n_jobs=None,\n",
       "                                                               oob_score=False,\n",
       "                                                               random_state=None,\n",
       "                                                               verbose=0,\n",
       "                                                               warm_start=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=6,\n",
       "             param_grid={'cls__criterion': ['gini', 'entropy'],\n",
       "                         'cls__max_depth': [5, 7, None],\n",
       "                         'cls__n_estimators': [100, 250, 500, 1000]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    \"cls__n_estimators\": [100, 250, 500, 1000],\n",
    "    \"cls__criterion\": [\"gini\", \"entropy\"],\n",
    "    \"cls__max_depth\": [5, 7, None]\n",
    "}\n",
    "\n",
    "pipe_rf = Pipeline([\n",
    "    ('var_tresh', VarianceThreshold(threshold=(.8 * (1 - .8)))),\n",
    "    ('feature_selection', SelectFromModel(lgbm.LGBMClassifier())),\n",
    "    ('cls', RandomForestClassifier())\n",
    "])\n",
    "\n",
    "grid_rf = GridSearchCV(pipe_rf, params, scoring='accuracy', n_jobs=6, cv=5)\n",
    "grid_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score on validation set (accuracy) = 0.7107\n",
      "best score on test set (accuracy) = 0.6760\n"
     ]
    }
   ],
   "source": [
    "preds = grid_rf.predict(X_test)\n",
    "print(\"best score on validation set (accuracy) = {:.4f}\".format(grid_rf.best_score_))\n",
    "print(\"best score on test set (accuracy) = {:.4f}\".format(accuracy_score(y_test, preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('scale',\n",
       "                                        StandardScaler(copy=True,\n",
       "                                                       with_mean=True,\n",
       "                                                       with_std=True)),\n",
       "                                       ('var_tresh',\n",
       "                                        VarianceThreshold(threshold=0.15999999999999998)),\n",
       "                                       ('feature_selection',\n",
       "                                        SelectFromModel(estimator=LGBMClassifier(boosting_type='gbdt',\n",
       "                                                                                 class_weight=None,\n",
       "                                                                                 colsample_bytree=1.0,\n",
       "                                                                                 importance_type='split',\n",
       "                                                                                 lear...\n",
       "                                            decision_function_shape='ovr',\n",
       "                                            degree=3, gamma='scale',\n",
       "                                            kernel='rbf', max_iter=-1,\n",
       "                                            probability=False,\n",
       "                                            random_state=None, shrinking=True,\n",
       "                                            tol=0.001, verbose=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=6,\n",
       "             param_grid={'cls__C': [0.5, 1, 2, 5],\n",
       "                         'cls__kernel': ['rbf', 'linear', 'sigmoid']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    \"cls__C\": [0.5, 1, 2, 5],\n",
    "    \"cls__kernel\": ['rbf', 'linear', 'sigmoid'],\n",
    "}\n",
    "\n",
    "pipe_svm = Pipeline([\n",
    "    ('scale', StandardScaler()),\n",
    "    ('var_tresh', VarianceThreshold(threshold=(.8 * (1 - .8)))),\n",
    "    ('feature_selection', SelectFromModel(lgbm.LGBMClassifier())),\n",
    "    ('cls', SVC())\n",
    "])\n",
    "\n",
    "grid_svm = GridSearchCV(pipe_svm, params, scoring='accuracy', n_jobs=6, cv=5)\n",
    "grid_svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score on validation set (accuracy) = 0.7613\n",
      "best score on test set (accuracy) = 0.7880\n"
     ]
    }
   ],
   "source": [
    "preds = grid_svm.predict(X_test)\n",
    "print(\"best score on validation set (accuracy) = {:.4f}\".format(grid_svm.best_score_))\n",
    "print(\"best score on test set (accuracy) = {:.4f}\".format(accuracy_score(y_test, preds)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results and save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, preds)\n",
    "classes = ['metal', 'disco', 'classical', 'hiphop', 'jazz', 'country', 'pop', 'blues', 'reggae', 'rock']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plot_confusion_matrix(cm, classes, normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Heitor Guimarães\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\externals\\joblib\\__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../models/pipe_svm.joblib']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(grid_svm, \"../models/pipe_svm.joblib\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
